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--- |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- zeroth |
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- generated_from_trainer |
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model-index: |
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- name: distilhubert-ko-zeroth |
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results: [] |
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language: |
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- ko |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilhubert-ko-zeroth |
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the BINGSU/ZEROTH-KOREAN - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9934 |
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- Cer: 0.2066 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| No log | 0.57 | 400 | 3.2681 | 0.6761 | |
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| 7.285 | 1.15 | 800 | 1.5312 | 0.4170 | |
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| 1.259 | 1.72 | 1200 | 1.3459 | 0.3846 | |
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| 0.9108 | 2.3 | 1600 | 1.1357 | 0.3239 | |
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| 0.7227 | 2.87 | 2000 | 1.0571 | 0.3056 | |
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| 0.7227 | 3.45 | 2400 | 1.0002 | 0.2829 | |
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| 0.5689 | 4.02 | 2800 | 0.8773 | 0.2553 | |
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| 0.4676 | 4.6 | 3200 | 0.8634 | 0.2462 | |
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| 0.3805 | 5.17 | 3600 | 0.8504 | 0.2323 | |
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| 0.2548 | 5.75 | 4000 | 0.8480 | 0.2260 | |
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| 0.2548 | 6.32 | 4400 | 0.8550 | 0.2231 | |
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| 0.189 | 6.9 | 4800 | 0.8587 | 0.2159 | |
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| 0.1336 | 7.47 | 5200 | 0.9012 | 0.2101 | |
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| 0.0827 | 8.05 | 5600 | 0.9302 | 0.2100 | |
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| 0.0506 | 8.62 | 6000 | 0.9622 | 0.2063 | |
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| 0.0506 | 9.2 | 6400 | 0.9826 | 0.2062 | |
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| 0.0389 | 9.77 | 6800 | 0.9933 | 0.2067 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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